The rise of LLMs (Large Language Models) has contributed to the improved performance and development of cutting-edge NLP applications. However, these can also pose risks when used maliciously, such as spreading fake news, harmful content, impersonating individuals, or facilitating school plagiarism, among others. This is because LLMs can generate high-quality texts, which are challenging to differentiate from those written by humans. GLTR, which stands for Giant Language Model Test Room and was developed jointly by the MIT-IBM Watson AI Lab and HarvardNLP, is a visual tool designed to help detect machine-generated texts based on GPT-2, that highlights the words in text depending on the probability that they were machine-generated. One limitation of GLTR is that the results it returns can sometimes be ambiguous and lead to confusion. This study aims to explore various ways to improve GLTR's effectiveness for detecting AI-generated texts within the context of the IberLef-AuTexTification 2023 shared task, in both English and Spanish languages. Experiment results show that our GLTR-based GPT-2 model overcomes the state-of-the-art models on the English dataset with a macro F1-score of 80.19%, except for the first ranking model (80.91%). However, for the Spanish dataset, we obtained a macro F1-score of 66.20%, which differs by 4.57% compared to the top-performing model.

Large language models (LLMs) are increasingly impacting human society, particularly in textual information. Based on more than 30,000 papers and 1,000 presentations from machine learning conferences, we examined and compared the words used in writing and speaking, representing the first large-scale study of how LLMs influence the two main modes of verbal communication and expression within the same group of people. Our empirical results show that LLM-style words such as "significant" have been used more frequently in abstracts and oral presentations. The impact on speaking is beginning to emerge and is likely to grow in the future, calling attention to the implicit influence and ripple effect of LLMs on human society.

Fake news is a major concern in our society right now. It has gone hand-in-hand with the rise of the data-driven era – not a coincidence when you consider the sheer volume of data we are generating every second! Fake news is such a widespread issue that even the world's leading dictionaries are trying to combat it in their own way. So what role has Machine Learning played in this? I'm sure you must have heard about a machine learning technique that generates fake videos mimicking famous personalities. Similarly, Natural Language Processing (NLP) techniques are being used to generate fake articles – a concept called "Neural Fake News". I've been working in the Natural Language Processing (NLP) space for the last few years and while I love the pace at which breakthroughs are happening, I'm also deeply concerned about the way these NLP frameworks are being used to create and spread false information.

Obviously, GLTR is not perfect. Its main limitation is its limited scale. It won't be able to automatically detect large-scale abuse, only individual cases. Moreover, it requires at least an advanced knowledge of the language to know whether an uncommon word does make sense at a position. Our assumption is also limited in that it assumes a simple sampling scheme.

Research is still being done on how to detect fake news without manual intervention. Detecting fake news by using stylometry-based provenance to track a text's writing style back to its first source has been accepted as one way to solve the challenge. Earlier, researchers from Harvard University and MIT-IBM Watson Lab had come up with an AI-powered tool to recognise AI-generated text. Known as the Giant Language Model Test Room (GLTR), the system works on finding out if a particular piece of writing was produced by a language model algorithm, aka computer or a human. With AI and natural language generation models being used to make fake news, GLTR can be used to differentiate machine-generated text from human-written text to a non-expert reader.

Researchers from Harvard University and MIT-IBM Watson Lab have created an AI-powered tool for spotting AI-generated text. Dubbed Giant Language Model Test Room (GLTR), the system aims to detect whether a specific piece of text was generated by a language model algorithm. You can give the tool a spin here. Don't miss Hard Fork Summit in Amsterdam With AI and natural language generation models already employed to produce fake news and spread misinformation, GLTR has the potential to distinguish machine generated text from human-written text to a non-expert reader. According to results shared by the researchers, GLTR improved the human detection-rate of fake text from 54 percent to 72 percent without any prior training.

This AI is one step ahead of... itself. Researchers at Harvard University and the MIT-IBM Watson AI Lab have created a tool to help combat the spread of misinformation. The tool, called GLTR (for Giant Language Model Test Room), uses artificial intelligence to detect the very statistical text patterns that give AI away, according to the team's June report. GLTR highlights words in the text based on the likelihood that they'll appear again -- green is the most predictable, red and yellow are less predictable, and the least predictable is purple. A tool like that could come in handy for social media sites like Twitter and Facebook that have to contend with rampant content created by bots.

AI algorithms can generate text convincing enough to fool the average human--potentially providing a way to mass-produce fake news, bogus reviews, and phony social accounts. Thankfully, AI can now be used to identify fake text, too. The news: Researchers from Harvard University and the MIT-IBM Watson AI Lab have developed a new tool for spotting text that has been generated using AI. Called the Giant Language Model Test Room (GLTR), it exploits the fact that AI text generators rely on statistical patterns in text, as opposed to the actual meaning of words and sentences. In other words, the tool can tell if the words you're reading seem too predictable to have been written by a human hand.

The rapid improvement of language models has raised the specter of abuse of text generation systems. This progress motivates the development of simple methods for detecting generated text that can be used by and explained to non-experts. We develop GLTR, a tool to support humans in detecting whether a text was generated by a model. GLTR applies a suite of baseline statistical methods that can detect generation artifacts across common Figure 1: The top-k overlay within GLTR. It is easy sampling schemes. In a human-subjects study, to distinguish sampled from written text. The real text we show that the annotation scheme provided is from the Wikipedia page of The Great British Bake by GLTR improves the human detection-rate Off, the fake from GPT-2 large with temperature 0.7. of fake text from 54% to 72% without any prior training.